23,408 research outputs found
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
Preprocessing and Content/Navigational Pages Identification as Premises for an Extended Web Usage Mining Model Development
From its appearance until nowadays, the internet saw a spectacular growth not only in terms of websites number and information volume, but also in terms of the number of visitors. Therefore, the need of an overall analysis regarding both the web sites and the content provided by them was required. Thus, a new branch of research was developed, namely web mining, that aims to discover useful information and knowledge, based not only on the analysis of websites and content, but also on the way in which the users interact with them. The aim of the present paper is to design a database that captures only the relevant data from logs in a way that will allow to store and manage large sets of temporal data with common tools in real time. In our work, we rely on different web sites or website sections with known architecture and we test several hypotheses from the literature in order to extend the framework to sites with unknown or chaotic structure, which are non-transparent in determining the type of visited pages. In doing this, we will start from non-proprietary, preexisting raw server logs.Knowledge Management, Web Mining, Data Preprocessing, Decision Trees, Databases
- …